Big Data Services

Big Data

Big Data Solutions

The commonly asked question pertaining to Big Data that millions of people have asked us is, “what does Big Data mean for my business?” Others who have been planning and investing for years describe a vast network of data collection and artificial intelligence driven analytics. Working in the field, we can see AI and Big data services evolving and progressing. In its current trajectory, it bears many similarities to the adoption of the internet.

Internet

As the first businesses adopted the internet, they gained a competitive advantage- a new front to gain exposure to customers. The internet also created new business: Uber, Amazon and Netflix, but it is difficult to predict when Big Data will be ready for entrepreneurs to do the same, after all, why did it take almost 3 decades for Amazon to get where it has?

Big Data

If the internet connects people, Big Data connects things. Big Data was the other half of the internet which was not possible to harness until recently. Analysis of data from the IoT will enable a taxi company to direct cars to areas where peak demand is predicted, or a parking company to automatically reserve you a spot, or a grocery store to provide instant delivery by predicting orders. Fundamentally, a company with Big Data capabilities will simply be able to offer a better or safer service to a customer for cheaper or the same price. Better services which customers will grow accustomed to and later demand of the firms without Big Data capabilities. Our view is to tap the first mover advantage and engage Big Data before the industry does.

Big Data Tools & Technology

Big Data Framework

A framework is a toolset used to solve complex issues. A framework comprises of a bunch of tools that fall under the same umbrella. A big data framework comprises of various big data tools that are not mutually exclusive and work well together to solve complex big data related tasks. Big Data frameworks can be used for data processing, analytics and solutions making use of big data engineering. Apache Hadoop and Apache Spark are two most famous big data frameworks available today. Apache Spark is the more preferred framework today. Both frameworks fall under the Apache umbrella, however the way Spark handles advanced data processing tasks like real time stream processing and machine learning is not possible to perform on Hadoop alone.

Big Data Architecture

A big data architecture is designed particularly when datasets are complex and big. The need for big data architecture arises when traditional databases cannot handle the volume of data. Primarily, what a big data architecture does is, it ingests, processes and analyzes data. Organizations will have varied needs while entering the big data space. This will depend on the capabilities of the users and the tools required. For a few organizations the need may be to analyze thousands of gigabytes whereas for others it may be analyzing thousands of terabytes.

Big Data Architecture to Optimizing Data Processing

Cloudera and its Applications

This bog tells you what cloudera is and gives
you simple and easy to follow steps on how
to configure it on AWS – EC2.Click Here →

Big data architecture is typically required when there is a need for:

Storing and processing data too large for traditional datasets.

Transformation of unstructured data with the aim of generating reports and for analysis.

Unbounded streams of data to be captured, processed and analyzed in real time.

Big Data Challenges

1.Complying to set regulations and adhering to privacy laws like GDPR.

2.Ensure that the data is secure because of the enormous volume of data that is stored and processed.

3.Managing the various sources of data is a big challenge. The volume of data produced from different sources and the velocity at which it is produced makes it difficult to manage.

4.Data storage becomes an issue due to the large amounts of data pouring in. This is when data lakes and data warehouses come into use, where large chunks of data are stored. However, the challenge with storage is when the data lakes/warehouses combine data from disparate sources and this results in errors, duplicates, logic conflicts etc.

5.The variety of big data technologies available makes it confusing to figure out which tool or framework will be suitable or whether a hybrid across frameworks would be required. Tech dilemmas like – Do you need Spark or will the speeds of Hadoop MapReduce be enough? – will always arise.

Big Data Value Proposition

1. Obtain “real actionable insights” with the use of Big Data Analytics. These insights will be based on evidences that have passed the proof of concept stage every time they have been put to the test.

2. Predictive analytics services will be able to predict outcomes even before occurrence based on analysis of past data. This will prove to be effective where corrective action needs to be taken or prevention of failure is the end game.

3. Big Data can be used to discover new business opportunities. The various channels of data may prove to be new ways of interacting with your clients. Data exploration may uncover new business segments, reasons for churn and forms of churn and many other opportunities.

4. Unstructured data will open a whole new world of expression. It is nothing but the expression of human language in words. NLP andtext analytics can be used well here to judge the sentiments of your customers and gain visibility into your customers’ expectations.

6. Move on from batch analytics and start accelerating your business into real-time operation by analyzing streaming big data.

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Big Data Challenges

1.Complying to set regulations and adhering to privacy laws like GDPR.

2.Ensure that the data is secure because of the enormous volume of data that is stored and processed.

3.Managing the various sources of data is a big challenge. The volume of data produced from different sources and the velocity at which it is produced makes it difficult to manage.

4.Data storage becomes an issue due to the large amounts of data pouring in. This is when data lakes and data warehouses come into use, where large chunks of data are stored. However, the challenge with storage is when the data lakes/warehouses combine data from disparate sources and this results in errors, duplicates, logic conflicts etc.

5.The variety of big data technologies available makes it confusing to figure out which tool or framework will be suitable or whether a hybrid across frameworks would be required. Tech dilemmas like – Do you need Spark or will the speeds of Hadoop MapReduce be enough? – will always arise.

Big Data Value Proposition

1. Obtain “real actionable insights” with the use of Big Data Analytics. These insights will be based on evidences that have passed the proof of concept stage every time they have been put to the test.

2. Predictive analytics services will be able to predict outcomes even before occurrence based on analysis of past data. This will prove to be effective where corrective action needs to be taken or prevention of failure is the end game.

3. Big Data can be used to discover new business opportunities. The various channels of data may prove to be new ways of interacting with your clients. Data exploration may uncover new business segments, reasons for churn and forms of churn and many other opportunities.

4. Unstructured data will open a whole new world of expression. It is nothing but the expression of human language in words. NLP andtext analytics can be used well here to judge the sentiments of your customers and gain visibility into your customers’ expectations.

6. Move on from batch analytics and start accelerating your business into real-time operation by analyzing streaming big data.

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100% Privacy Guaranteed

Big Data Testing

Big Data is not a buzzword anymore. Most organizations have started adopting to Big Data and it has become an integral part of the organization’s decision-making process. With huge quantities of data at their disposal, mostly unstructured data, organizations are struggling to get the best out the data in hand. Data comes in all form, with volume and velocity, and the data is updated at a rapid pace hence the processing time should be quicker too.

How QA testing benefits Big data

Data has grown exponentially most recent years, and it will keep on growing. In the past, preparing a few million records was viewed as a huge assignment, which may eventually incur significant damage on the decision-making. However now with the help of Big Data, this is not an uphill task anymore.

As data grows, the challenges grow as well. Below are some of the challenges organization faces with huge volume of unstructured data,